article

Time-series forecasting using flexible neural tree model

Authors:

Ajith AbrahamAuthors Info & Claims

Information Sciences—Informatics and Computer Science, Intelligent Systems, Applications: An International Journal, Volume 174, Issue 3-4

Pages 219 - 235

Published: 11 August 2005 Publication History

Abstract

Time-series forecasting is an important research and application area. Much effort has been devoted over the past several decades to develop and improve the time-series forecasting models. This paper introduces a new time-series forecasting model based on the flexible neural tree (FNT). The FNT model is generated initially as a flexible multi-layer feed-forward neural network and evolved using an evolutionary procedure. Very often it is a difficult task to select the proper input variables or time-lags for constructing a time-series model. Our research demonstrates that the FNT model is capable of handing the task automatically. The performance and effectiveness of the proposed method are evaluated using time series prediction problems and compared with those of related methods.

References

[1]

Omatu, S., Khalid, Marzuki and Yusof, Rubiyah, Neuro-Control and its Applications. 1996. Springer Publisher.

[2]

Fahlman, S.E. and Christian Lebiere, The cascade-correlation learning architecture. Advances in Neural Information Processing Systems. v2. 524-532.

[3]

Nadal, J.-P., Study of a growth algorithm for a feedforward network. International Journal of Neural Systems. v1. 55-59.

[4]

Setiono, R. and Hui, L.C.K., Use of a quasi-Newton method in a feedforward neural network construction algorithm. IEEE Transactions on Neural Networks. v6. 273-277.

[5]

Angeline, P.J., Saunders, Gregory M. and Pollack, Jordan B., An evolutionary algorithm that constructs recurrent neural networks. IEEE Transactions on Neural Networks. v5. 54-65.

[6]

Yao, X. and Liu, Y., A new evolutionary system for evolving artificial neural networks. IEEE Transactions on Neural Networks. v8. 694-713.

[7]

Yao, X., Evolving artificial neural networks. Proceedings of the IEEE. v87. 1423-1447.

[8]

Yao, X., Liu, Y. and Lin, G., Evolutionary programming made faster. IEEE Transactions on Evolutionary Computation. v3. 82-102.

[9]

Stanley, Kenneth O. and Risto Miikkulainen, Evolving neural networks through augmenting topologies. Evolutionary Computation. v10. 99-127.

[10]

Zhang, B.T., Ohm, P. and Muhlenbein, H., Evolutionary induction of sparse neural trees. Evolutionary Computation. v5. 213-236.

[11]

Zhang, G. Peter, Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing. v50. 159-175.

[12]

Salustowicz, R.P. and Schmidhuber, J., Probabilistic Incremental Program Evolution. Evolutionary Computation. v2 i5. 123-141.

[13]

Chen, Y. and Kawaji, S., System identification and control using probabilistic incremental program evolution algorithm. Journal of Robotics and Machatronics. v12. 675-681.

[14]

S. Baluja, Population-based incremental learning: a method for integrating genetic search based function optimization and competitive learning, Technical Report CMU-CS-94-163, Carnegie Mellon University, Pittsburgh, 1994.

Digital Library

[15]

E.K. Burke, Y. Bykov, J.P. Newall, S. Petrovic, A new local search approach with execution time as an input parameter, Technical Report No. NOTTCS-TR-2002-3, School of Computer Science and Information Technology, University of Nottingham, 2002.

[16]

Kirkpatrick Jr., S., Gelatt, C.D. and Vecchi, M.P., Optimization by simulated annealing. Science. v220. 671-680.

[17]

Working Group on Data Modeling Benchmark, Standard Committee of IEEE Neural Network Council. <http://neural.cs.nthu.edu.tw/jang/benchmark/>, 2004 (accessed 14.10.04).

[18]

Box, G.E.P., Time Series Analysis, Forecasting and Control. 1970. Holden Day, San Francisco.

[19]

Tong, R.M., The evaluation of fuzzy models derived from experimental data. Fuzzy Sets and Systems. v4. 1-12.

[20]

Pedtycz, W., An identification algorithm in fuzzy relational systems. Fuzzy Sets and Systems. v13. 153-167.

[21]

Xu, C.W. and Lu, Y.Z., Fuzzy model identification and self-learning for dynamic systems. IEEE Transactions on Systems, Man and Cybernetics. v17. 683-689.

[22]

M. Sugeno, T. Takagi, Linguistic modelling based on numerical data, Proceedings of the IFSA'91, 1991.

[23]

H. Surmann, A. Kanstein, K. Goser, Self-organising and genetic algorithm for an automatic design of fuzzy control and decision systems, Proceedings of the FUFIT's93 (1993) 1079-1104.

[24]

Sugeno, M. and Takagi, T., A fuzzy-logic approach to qualitative modeling. IEEE Transactions on Fuzzy Systems. v1. 7-31.

[25]

Lee, Y.-C., Hwang, E. and Shih, Y.-P., A combined approach to fuzzy model identification. IEEE Transactions on Systems, Man and Cybernetics. v24. 736-744.

[26]

W. Hauptmann, A neural net topology for bidirectional fuzzy-neuro transformation, Proceedings of the IEEE International Conference on Fuzzy Systems (1995) 1511-1518.

[27]

Lin, Y. and Cunningham, G.A., A new approach to fuzzy-neural system modelling. IEEE Transactions on Fuzzy Systems. v3. 190-197.

[28]

Nie, J., Constructing fuzzy model by self-organising counter propagation network. IEEE Transactions on Systems Man and Cybernetics. v25. 963-970.

[29]

Jang, J.-S.R., Sun, C.-T. and Mizutani, E., Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence. 1997. Prentice-Hall, Upper Saddle River, NJ.

[30]

Kasabov, N., Kim, J.S., Watts, M. and Gray, A., FuNN/2-a fuzzy neural network architecture for adaptive learning and knowledge acquisition. Information Science. v101. 155-175.

[31]

Kim, J. and Kasabov, N., HyFIS: adaptive neuro-fuzzy inference systems and their application to nonlinear dynamical systems. Neural Networks. v12. 1301-1319.

[32]

Cho, K.B. and Wang, B.H., Radial basis function based adaptive fuzzy systems their application to system identification and prediction. Fuzzy Sets and Systems. v83. 325-339.

[33]

Wang, L.X. and Mendel, J.M., Generating fuzzy rules by learning from examples. IEEE Transactions on Systems, Man and Cybernetics. v22. 1414-1427.

[34]

Rojas, I., Pomares, H. and Bernier, J. Luis, Time series analysis using normalized PG-RBF network with regression weights. Neurocomputing. v42. 267-285.

[35]

Kim, D. and Kim, C., Forecasting time series with genetic fuzzy predictor ensembles. IEEE Transactions on Fuzzy Systems. v5. 523-535.

[36]

Li, X. and Yu, W., Dynamic system identification via recurrent multilayer perceptions. Information Science. v147. 45-63.

[37]

Horng, J.-H., Neural adaptive tracking control of a DC motor. Information Sciences. v118. 1-13.

[38]

Kirschner, H. and Hillebr, R., Neural networks for HREM image analysis. Information Sciences. v129. 31-44.

[39]

Sheta, A.F. and Jong, K.D., Time-series forecasting using GA-tuned radial basis functions. Information Science. v133. 221-228.

[40]

Snchez, L., Cousob, I. and Corrales, J.A., Combining GP operators with SA search to evolve fuzzy rule based classifiers. Information Sciences. v136. 175-191.

[41]

Yeun, Y.S., Suh, J.C. and Yang, Y.S., Function approximations by superimposing genetic programming trees: with applications to engineering problems. Information Sciences. v122. 259-280.

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Jarraya YBouaziz SAlimi AAbraham A(2020)Hierarchical fuzzy design by a multi-objective evolutionary hybrid approachSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-019-04129-624:5(3615-3630)Online publication date: 1-Mar-2020
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cover image Information Sciences: an International Journal

Information Sciences: an International Journal Volume 174, Issue 3-4

11 August 2005

177 pages

ISSN:0020-0255

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Copyright © Elsevier Inc. © 2004.

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Elsevier Science Inc.

United States

Publication History

Published: 11 August 2005

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Cited By

Xia HTang JQiao JZhang JYu W(2022)DF classification algorithm for constructing a small sample size of data-oriented DF regression modelNeural Computing and Applications10.1007/s00521-021-06809-734:4(2785-2810)Online publication date: 1-Feb-2022
https://dl.acm.org/doi/10.1007/s00521-021-06809-7
Jarraya YBouaziz SAlimi AAbraham A(2020)Hierarchical fuzzy design by a multi-objective evolutionary hybrid approachSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-019-04129-624:5(3615-3630)Online publication date: 1-Mar-2020
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Abpeykar SGhatee M(2019)An ensemble of RBF neural networks in decision tree structure with knowledge transferring to accelerate multi-classificationNeural Computing and Applications10.1007/s00521-018-3543-931:11(7131-7151)Online publication date: 1-Nov-2019
https://dl.acm.org/doi/10.1007/s00521-018-3543-9
Ojha VSchiano SWu CSnášel VAbraham A(2018)Predictive modeling of die filling of the pharmaceutical granules using the flexible neural treeNeural Computing and Applications10.1007/s00521-016-2545-829:7(467-481)Online publication date: 1-Apr-2018
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Bao WWang DChen Y(2017)Classification of Protein Structure Classes on Flexible Neutral TreeIEEE/ACM Transactions on Computational Biology and Bioinformatics10.1109/TCBB.2016.261096714:5(1122-1133)Online publication date: 1-Sep-2017
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Ojha VAbraham ASnel V(2017)Ensemble of heterogeneous flexible neural trees using multiobjective genetic programmingApplied Soft Computing10.1016/j.asoc.2016.09.03552:C(909-924)Online publication date: 1-Mar-2017
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Chen ZPeng LGao CYang BChen YLi J(2017)Flexible neural trees based early stage identification for IP trafficSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-015-1902-321:8(2035-2046)Online publication date: 1-Apr-2017
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Wu POrchard J(2017)Using Flexible Neural Trees to Seed BackpropagationNeural Information Processing10.1007/978-3-319-70087-8_12(109-116)Online publication date: 14-Nov-2017
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Yang BChen Y(2016)Somatic mutation detection using ensemble of flexible neural tree modelNeurocomputing10.1016/j.neucom.2015.12.001179:C(161-168)Online publication date: 29-Feb-2016
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